1. Field of the Invention
The present invention relates to an authentication apparatus, an authentication method, and storage medium for personal authentication, and more particularly to an authentication apparatus that performs personal authentication by checking input data against dictionary data stored in advance in a dictionary.
2. Description of the Related Art
In general, to perform personal authentication, authentication element data, such as faces, voices or fingerprints of a plurality of persons, are stored in advance in a dictionary as dictionary data. Then, the personal authentication is performed by comparing input data of an object to be authenticated and authentication element data items stored in the dictionary, and identifying an authentication element data item of a person which corresponds to the input data based on the result of the comparison.
In the personal authentication of this type, when a face image indicative of the face of a person is used as an authentication element data item, a face orientation and facial expressions of the face image, and a change in an optical environment when the face image is shot has large influence on the accuracy of the personal authentication. Here, the term “change in an optical environment” is intended to mean differences in light and shade caused by direct light, backlight, and side light, for example.
To perform personal authentication using a face image, there has been proposed a technique which uses auxiliary feature value data items formed by classifying, according to different shooting conditions, a plurality of feature value data items extracted from face image data of a plurality of persons, shot in the different shooting conditions (see Japanese Patent Laid-Open Publication No. 2008-146539). In this technique disclosed in Japanese Patent Laid-Open Publication No. 2008-146539, personal authentication is performed on a face image as input data with high accuracy using the auxiliary feature value data items, irrespective of the shooting environment of the face image.
Further, to accurately perform personal authentication even in environments different in shooting conditions, there has been proposed a technique which extracts corresponding part features between input patterns and registered patterns, which are registered in advance, and generates a plurality of combinations of the part features as combined part features (see Japanese Patent Laid-Open Publication No. 2011-100229). In this technique disclosed in Japanese Patent Laid-Open Publication No. 2011-100229, the degrees of similarity between the input patterns and the registered patterns are calculated using the combined part features, and personal authentication is executed according to the degrees of similarity.
However, in the method disclosed in Japanese Patent Laid-Open Publication No. 2008-146539, when it is taken into account that shooting conditions undergo complex changes including environmental changes, if auxiliary feature value data items as well are all stored as dictionary data for each of different shooting conditions including different environments which vary, burden of storage processing on the user becomes very heavy. Further, an increase in the amount of dictionary data causes an increase in time taken to execute processing for personal authentication. Furthermore, to create dictionary data, the user is required to know image shooting conditions.
Further, in the method disclosed in Japanese Patent Laid-Open Publication No. 2011-100229, combined part features are generated from part features corresponding between registered patterns and input patterns, and hence the calculated degree of similarity changes depending on the number of the registered patterns. This change in the degree of similarity sometimes causes erroneous authentication. More specifically, small areas of face images of the same person, stored as dictionary data, are sometimes displaced between the images, which can largely change features of the face images to cause erroneous recognition.
FIG. 10 is a diagram useful in explaining erroneous recognition caused by the conventional personal recognition method.
Now, let it be assumed that face images (hereinafter also simply referred to as the “faces”) 701 to 704 are of the face of the same person. The feature of a small area 712 of the face 702 largely changes from the feature of a small area 711 of the face 701, due to a change in the orientation of the face. Further, in the case of a small area 713 of the face 703, light affects positioning of an organ detection position where an organ, such as eyes, a nose, or the like is detected for deciding the position of the small area 713, causing displacement of the organ detection position, so that the position of the small area 713 is displaced.
Further, in the case of a small area 714 of the face 704, the feature of the small area 714 is changed by an accessory, such as a mask 715 (or eyeglasses (not shown)).
As described above, when small areas of face images of the same person are displaced between the face images, the feature of the small area largely changes, which causes erroneous recognition.
FIGS. 11A and 11B are diagrams useful in explaining calculation of the degree of similarity, performed when face images of the same person shown in FIG. 10 are stored as dictionary data items indicative of a specific person.
In FIGS. 11A and 11B, the faces 701 to 704 shown in FIG. 10 are stored as data items of “Mr. A” in the dictionary data (here, they are shown as face images (hereinafter, also simply referred to as the “faces”) 810 to 840). Further, let it be assumed here that a face image 850 is stored as an data item of “Mr. B” in the dictionary data, and the degree of similarity between a face image 800 of Mr. B as an object to be authenticated and an data item of the dictionary data is calculated.
Now, a small area 801 is set in the face image 800 as the object to be authenticated, and is compared with associated small areas 811 to 841 of the face images 810 to 840. As described above, there are displacements between the respective small areas 811 to 841 of the face images 810 to 840. When there are such displacements, the degree of similarity between any of the small areas 811 to 841 and the small area 801 sometimes becomes high.
Further, let it be assumed that when another small area 802 is set in the face image 800 as the object to be authenticated and is compared with respective corresponding small areas 812 to 842 of the face images 810 to 840, the degree of similarity between any of the small areas 812 to 842 and the small area 802 sometimes becomes high, similarly to the case of the small area 801, provided that there are displacements between the small areas 812 to 842.
To perform the personal authentication, small areas 851 and 852 are similarly set in the face image 850, and the face image 800 as the object to be authenticated and the face image 850 are also compared. In this case, a similarity degree r891, which is obtained by comparing the face image 800 with one of the face images 810 to 840 which are faces of a person other than the person of whom the face image 800 is picked up belongs can be sometimes larger than a similarity degree r892, which is obtained by comparing the face image 800 and a face image of the same person of whom the face image 800 is picked up. This causes a face image of another person to be erroneously authenticated as the face image 800.
More specifically, when the number of registered face images (i.e. dictionary data items) associated with the same person increases according to changes in shooting conditions and the like, not only the degree of similarity between the registered face images and a face image of the same person but also the degree of similarity between the registered face images and a face image of another person sometimes become high. This sometimes causes erroneous authentication.